class_docstr: Helper class to slice 3d volume into smaller volumes and merge them back
class_docstr_tok: ["Helper","class","to","slice","3d","volume","into","smaller","volumes","and","merge","them","back"]
code: def merge(self, tiles: List[np.ndarray], dtype=np.float32):
      if len(tiles) != len(self.crops):
          raise ValueError
  
      channels = 1 if len(tiles[0].shape) == 2 else tiles[0].shape[2]
      target_shape = (
          self.image_height + self.margin_bottom + self.margin_top,
          self.image_width + self.margin_right + self.margin_left,
          channels,
      )
  
      image = np.zeros(target_shape, dtype=np.float64)
      norm_mask = np.zeros(target_shape, dtype=np.float64)
  
      w = np.dstack([self.weight] * channels)
  
      for tile, (x, y, tile_width, tile_height) in zip(
          tiles, self.crops
      ):
          # print(x, y, tile_width, tile_height, image.shape)
          image[y : y + tile_height, x : x + tile_width] += tile * w
          norm_mask[y : y + tile_height, x : x + tile_width] += w
  
      # print(norm_mask.min(), norm_mask.max())
      norm_mask = np.clip(
          norm_mask, a_min=np.finfo(norm_mask.dtype).eps, a_max=None
      )
      normalized = np.divide(image, norm_mask).astype(dtype)
      crop = self.crop_to_orignal_size(normalized)
      return crop
  
file_path: pytorch-toolbelt/pytorch_toolbelt/inference/tiles_3d.py
imports: from typing import Union, Tuple, List, Iterable, Any
  import numpy
  import torch
  from pytorch_toolbelt.inference import compute_pyramid_patch_weight_loss
label_desc: Use image node_id 5 for calling the VolumeSlicer obj's underlying member method code with example usage: obj.merge(tiles, dtype) and returns: crop